The future is murky...

On New Year’s Eve, we generally like to take stock of where we are, take a deep breath, prepare for a fresh new year with some resolutions, and of course, make wild predictions about the future.

IBM is getting into the spirit of the season with its 5 in 5, a list of five innovations that will change our lives in five years.  This is the sixth year that the company has released its list of predictions, which are driven by market data, social trends, and innovations taking place in IBM research labs.

Before we look ahead, let’s look at how IBM has done with its past predictions.  In 2006 IBM predicated that, by 2011, we would have digitized medical records and be using advanced video teleconferencing systems to speak and interact with our doctors.  We are not completely there, but we are on the way.  The company also predicted context-aware mobile devices and nanotechnology being used to control our environment.  Mobile devices have certainly evolved in that direction, and we are using advanced nanotechnology to improve solar energy collection.

In the fail column, IBM also believed that, by 2011, we would be immersed in a 3-D Internet (Snow Crash, anyone?) and that real-time translation (Star Trek-style) would be possible.

In the area of Information Overload, we previously predicted that Information Overload would continue to increase despite attempts by us and on the part of others to raise awareness of the high cost and the negative impact it has.  Unfortunately, we were correct in our prediction and the amount of Information Overload rose in lockstep with the increase in the amount of information created over the past year.

Looking to the year ahead, the trend will continue and we can expect more of the same, namely more information and more Information Overload.

This year, in its 5 in 5 forecast and on a more positive note, IBM is banking on the following:

1.)  People power.  Advances in renewable energy technology will allow for the harnessing of kinetic energy from movement such as walking or jogging, or even residual heat from individuals or machines.

2.)  Multi-factor biometrics.  Passwords will become obsolete as we increasingly rely on identification via biometric data such as facial definitions, retinal scans, and voice recognition.

3.)  Mind reading.  No, really.  Bioinformatics is the field of harnessing electronic brain activity with advanced sensors to understand facial expressions, concentration levels, and thoughts of a person.  The technology can be applied to controlling mobile devices, medical testing, and the gaming industry.

4.)  Death of the digital divide.  IBM believes that the ubiquity of mobile devices will all but eliminate the gap between those who have information access and those who do not.  The company estimates that in the next five years there will be 5.6 billion mobile devices sold, giving 80% of the 7 billion people on earth access to such a device.

5.)  Junk mail will become useful?  With both spam filters and targeted advertising becoming more precise, IBM thinks that real-time analytics will become so advanced that the technology will be able to accurately determine what you really want.  An example of this kind of predictive, targeted advertising would be reserving concert tickets for your favorite band on a night that you have a free space on your calendar, all without asking you.

This year’s 5 in 5 predictions are interesting and fun, and it is easy to see how trends support some of the ideas.  Biometrics, control of technology via brain waves, and harnessing kinetic energy in particular seem very plausible.  Eliminating the digital divide that separates the information haves from the have nots and solving the junk mail problem seem a bit trickier, but in the spirit of the holidays lets be optimistic.

Cody Burke is a senior analyst at Basex.  He can be reached at cburke@basex.com

(Image courtesy of John Stephen Dwyer)

You have very pretty lights TrapIt, but can you find me good content?

Finding relevant content is tricky business; not only is there an abundance of irrelevant content to confuse us and muddy the waters, but we often simply don’t know something exists until we see it.  Traditional search fails us here because that method assumes we know what we are looking for in the first place, or that we possess the forethought to make some assumptions and pick out some key words to enter into the search bar.  Perhaps what we really need are intelligent tools that suggest things to us before we even know we are looking for them.

TrapIt, a new online content discovery tool, aims to meet that need.  TrapIt was developed by the same minds that created and then sold Siri to Apple (the Siri technology has now been fully integrated into the iPhone 4S).  Both offerings leverage artificial intelligence (AI) technology that was developed as part of the CALO (Cognitive Agent that Learns and Organizes) project, an AI project funded by the Defense Advanced Research Projects Agency (DARPA).   Originally intended for military use, the cognitive software was designed to learn from experience, take orders, explain its own actions, and respond to unexpected input.

The key difference between the two is that Siri is a virtual assistant that responds to your verbal commands to do tasks such as setting reminders or searching for information while TrapIt is designed to seek out the things that it has determined you will find relevant, without you having to ask for them.

TrapIt is currently in beta and delivers personalized content from over 100,000 sources based on “traps”, which are essentially search terms that the user sets up.  Once created, a trap will automatically refresh itself with new content to be read when the user logs in, building a personalized homepage that reflect the user’s interests.  Creating the trap “tablet usage” for instance creates a stream of content that relates to tablets and usage data.  The user is then prompted to give thumbs up or down to the content to indicate whether it is what he was looking for or  not.  When giving a thumbs down, there are options to indicate why, such as because it was not interesting, the source was not trusted, or the content was spam.  This helps to further refine the content that is suggested going forward.

The content that TrapIt collects is refined as TrapIt analyzes how often the user clicks on specific types of content, as well as through the thumbs up or down mechanism.    Because the AI learns as TrapIt is used, it is too early to tell from my tests how effective it will be at providing relevant content, but results so far are encouraging.

Siri, the first commercial application of the CALO AI, has been well received and is quickly becoming a popular feature of the iPhone (as well as being hacked to run on older iPhone models and platforms such as Android, or even to control thermostats).  Now that TrapIt is applying the same underlying technology to content discovery, we will have a chance to see how effective the AI really is, and if it can recommend content in a way that helps to cut through the clutter of information and get us the information we really need.

Cody Burke is a senior analyst at Basex.  He can be reached at cburke@basex.com

Just one more text...

If you’re staying up late reading and/or writing e-mail, doing work, reading online forums and news, catching up on social networks – and then find yourself sleep-deprived the next day, you’re not alone.

Sleep deficiency is a major problem for knowledge workers who, due to increased mobility, are now more likely then ever to continue their work from home after leaving the office.  Even setting aside the pressure (some would even say the necessity) of answering e-mail messages or working on projects late into the night, the temptation of late-night recreational Internet and technology use is omnipresent.

Such behavior is considered poor sleep hygiene, a term that refers to one’s habits and practices at bedtime as well as environmental factors that may influence the length and quality of one’s sleep.

Consider the following statistics from the National Sleep Foundation’s 2011 “Sleep in America” poll:

- 39% of Americans bring their mobile phone into bed with them and end up using it in the hour before they go to sleep.  The number is even higher for younger Americans, 67% of 19-29 year olds.  21% of Americans end up texting during this time.

- Those individuals that end up texting in the hour before sleep are more likely to report bad sleep and not feeling refreshed.

- 1 in 10 Americans is woken up by mobile phone alerts from texts, calls, and e-mail.  The number rises to nearly 1 in 5 for 19-29 year olds.

- 36% of Americans use their laptop in bed before they go to sleep, and this group reports that it is less likely to get a good night’s sleep.

Why does this matter?  Surely we are able to deal with the loss of a little sleep in exchange for getting out that important e-mail or sending that last text of the day, right?

Unfortunately not, according to the UK-based Mental Health Foundation.  The organization’s 2011 “Sleep Matters” report notes that individuals who experience even mild sleep disorders are four times more likely to have relationship problems, three times more likely to lack concentration during their work day, three times more likely to struggle to accomplish tasks at work or during their day, and over twice as likely to suffer from energy deficiency.

Last week (in case you missed it due to being tired) was Drowsy Driving Prevention Week, a public awareness campaign by the National Sleep Foundation to highlight the issue of sleep safety.  To put the problem in perspective, the National Highway Traffic Safety Administration estimates that drowsy driving results in 1,550 deaths, 71,000 injuries and more than 100,000 accidents each year.  The National Sleep Foundation estimates that drowsy driving is involved in about one in six deadly crashes and one in eight crashes resulting in hospitalization.

Presuming you are not nodding off while reading this, consider how lack of sleep might be impacting your productivity and effectiveness.  Before going to sleep, lay off e-mail and texting, and maybe even try just turning all of your devices completely off.

Sweet dreams.

Cody Burke is a senior analyst at Basex.  He can be reached at cburke@basex.com

At your service.

Although Apple’s keynote presentation last week left many underwhelmed (no iPhone 5), there was one bright spot for knowledge workers: Siri.  The new feature is a voice activated virtual assistant that allows users to use voice commands to control functions on the iPhone such as speech-to-text, calendaring, and calling.  More significantly, Siri features natural language processing that enables users to make queries and searches and receive answers.

Search as we know it is significantly limited by its design.  Typical searches return correct result sets, meaning a list of sources that meets the search criteria.  Unfortunately, searches typically don’t return correct answers; you must do that part of the work yourself by combing through the result set.  Not only is this time consuming, but the process of sorting through the search results opens up the possibility of ending up in selecting and using the wrong information.

During Apple’s announcement, the company showed a demo video of Siri that included a woman asking her iPhone “Is it going to be chilly inSan Franciscothis weekend?” Siri responded “Not too cold, maybe down to 61 degrees.”  She asked a question, and got an answer.  Typically, she would have accessed Google on her smartphone or PC and typed “San Franciscoweather.”  She would have been rewarded with links to weather sites, as well as a relatively handy weather forecast image at the top of the results page.  She would have gotten the information she needed, but not in the same way.  A traditional search for the information would have returned correct results, but not the specific answer to her real question.

Voice activated commands are nothing new, and Siri’s original iPhone app has been around since February 2010.  Apple acquired the company in April of 2010, and has given the underlying virtual assistant technology the Apple design treatment, tightly integrating it into all areas of the iPhone 4S.  Siri is activated by holding down the home button, and then asking a question or giving a voice command.  Siri will ask clarification questions if needed until it has the information it requires for the task.  For fact checking, Siri leverages connections to sources such as Wikipedia and Wolfram Alpha.

Siri has roots in cognitive software with artificial intelligence incorporated into its code, which was originally developed by the Stanford Research Institute in conjunction with the Defense Advanced Research Projects Agency (DARPA).  Originally dubbed CALO (Cognitive Agent that Learns and Organizes), the project’s goal was to develop software for interrelated decision-making tasks that had previously been resistant to automation.  To succeed, the cognitive software needed to learn from experience, take orders, explain its own actions, and respond to unexpected input.

Despite its serious sounding roots, Siri on the iPhone 4S seems destined to be used for finding restaurants, booking movie tickets, and voice automation tasks such as speech-to-text and controlling phone functions.  However, the potential of the tool to revolutionize how we think about search is tremendous.  Apple has thrown its weight behind the virtual assistant concept and, if the company’s past successes are any indication, there is a good chance that others will follow.  Existing companies in the space, such as Nuance, could also find increased interest in their offerings.

Improving the search experience for knowledge workers and consumers is sorely needed, and with Apple’s considerable backing, Siri just might be the first step in a larger evolution of search that emphasizes correct answers over correct results.

Cody Burke is a senior analyst at Basex.  He can be reached at cburke@basex.com

At your service...

Uncoupling your physical presence from your office and working remotely is no longer a pipe dream; increasingly high numbers of knowledge workers are doing it everyday.  For over a decade now, technology has allowed off site workers to access e-mail, chat via instant messaging, and attend online meetings from anywhere with an Internet connection.

But what about keeping tabs on what is happening on the factory floor, or popping into the ad hoc meetings that take place in a co-worker’s office?  For that, you need your own telepresence robot, and unfortunately they tend to be very expensive.

Your basic telepresence robot comes with a basic set of features that includes a camera, display screen, microphone, and speakers.  Prices vary widely.  The QB telepresence robot from Anybots runs $15,000 and features automatic obstacle avoidance.  A cheaper model from Vgo costs $6,000, and you can even make an appointment and test drive one in their office via their Web site.  In the pipeline, iRobot, the maker of the Roomba robot vacuum cleaners and the military PackBot, is working on Ava, a robot that uses an Android tablet as its head and brains.  For users who need to be able to move around an office or factory floor and visually see what is going on and interact with people. For users with disabilities, which prevent them from going to work or moving about the office once there, robotic telepresence may be appealing and very useful.

Not all solutions are expensive however.  This past weekend I attended Maker Faire NYC.  For the uninitiated, Maker Faire is an event sponsored by Make Magazine (a Do-It-Yourself magazine) to “celebrate arts, crafts, engineering, science projects and the DIY mindset.”  The event is a sort of high school science fair on steroids; it features exhibits, speakers, demonstrations, workshops, and showcases for new and emerging technologies.

One project presented at the event was a DIY telepresence robot dubbed MAYA (Me And You Everywhere).  Designed and built by Ben Hylak, a 14 year old student, the robot uses a Roomba as its platform and an Acer Aspire netbook running RoboRealm software for control.  Two-way audio and video communication is enabled via Skype and built-in speakers and a Webcam.  The body of the robot is an upside-down plastic trashcan (which looks surprisingly good), with a 15” LCD display mounted on top.  The robot also features object recognition that Ben programmed to identify different kinds of pills (he envisions healthcare applications), and an articulated arm with a gripping device.

The cost of Ben’s MAYA robot?  Under $500, and he is selling the kits that would allow you to make one at home if you are feeling handy and want a weekend project.  Just think, you could send your new robot to work in your stead next week.

I don’t necessarily expect any executives to take the cheap route and place bulk orders for a DIY telepresence robot made from a vacuum cleaner and a plastic trashcan.  However, as demonstrated by this motivated and obviously intelligent 14 year old, the tools for implementing robotic telepresence at a reasonable price point are out there.  Expect to see more of these kinds of robots in the near future, and don’t be surprised when you run into one at the water cooler.

Cody Burke is a senior analyst at Basex.  He can be reached at cburke@basex.com

Bow-ties: classy and helpful for understanding Information Overload

Information Overload is something that most knowledge workers understand intuitively.  We all know what it feels like to stare at an overflowing inbox not unlike a deer in headlights, or to sit at your desk wracking your brain trying to remember the location of an important bit of information.

Unfortunately, although it is easy to recall the feeling of Information Overload, visualizing and conceptualizing it is much more difficult.  We get hung up in definitions, specific technologies, and different approaches to dealing with the problem, and as a result, often fail to see what the problem looks like on a simplified, macro level.  We understand what Information Overload is, but fail to see the forest for the trees.

To help visualize the complex information flows that every knowledge worker and every organization must navigate, I often use the metaphor of a bow-tie.  This helps me to understand and conceptualize Information Overload, and it may be helpful to you as well.  To start, simply picture the shape of a bow-tie (yes, the fancy one that goes around the neck).

On the left side of the bow-tie is the complex incoming information in the form of communications, news and reports, meetings, and any other information input, no matter how small.  This flow includes sources both internal and external to the organization that are filtered down and processed in the middle of the bow-tie, the knot.  The knot is where the complex flow of information is reduced, simplified, and digested so that it can be used to produce complex outcomes on the other side of the knot.  The right side of the bow-tie is where the structured and digested information is applied to business problems and used to create profit and gain advantage.

The bow-tie is a powerful model because it allows for complex inputs to be reduced to manageable blocks that are then used to drive complex outcomes.  The problem is that the knot of the bow-tie, and by extension the organization, team, or individual knowledge worker, is vulnerable to becoming overloaded.  If the knot fails and is overwhelmed by the incoming information on the left side, then the important outcomes being produced on the right side will suffer.

For example, imagine a knowledge worker (sitting in the middle, at the knot) who is dealing with too much information in the form of extremely high numbers of search results (the incoming information on the left-hand side of the bow-tie).  The combination of his inadequate search tools and techniques leads him to becoming overwhelmed.  As a result, he is not able to find the information he is looking for, and he moves forward with his project using sub-standard information.  The project (the output on the right-hand side of the bow-tie) ends up having to be redone and reviewed many more times than necessary because of the errors.  If the problem at the knot could have been avoided, the significant time and effort that was spent fixing the errors would have been saved.

On a group level, a sales team that is receiving an overwhelming amount of e-mail will be unable to effectively process the incoming information, leading to the team missing promising sales leads.  The team’s failure to maximize the information they are receiving leads to the outputs that are produced being not up-to-par, in this case resulting in lower sales.  The problem in this case is the team’s ability to process the high volume of e-mail effectively; resolving that pain point would improve the output and drive to higher sales.

Now, I don’t expect anyone to start wearing bow-ties because of this Information Overload visualization technique (although they are very stylish and perhaps underused).  Nonetheless, applying this metaphor to areas where Information Overload is harming productivity and impacting an organization’s bottom line may help to understand the problem and focus efforts to address it.

Cody Burke is a senior analyst at Basex.  He can be reached at cburke@basex.com

Search 101?

Blaming search tools for failures in search is common, and more often than not without merit.  It often seems as if Google has failed us when we search for content that we know exists, yet the search results taunt us by refusing to reveal what we are looking for.

The truth however, is that we are simply incredibly bad at using search tools.  Two items came to my attention this week that demonstrates our ineptitude at finding what we are looking for.  The first was a statistic derived from research by Dan Russell, a search anthropologist at Google, who was interviewed by Alexis Madrigal for the Atlantic.  Russell studies how computer users perform searches, and found that 90% of computer users do not know how to use CTRL + F (PC) or Command + F (Mac) to search through a document or Web page.

The time that is surely wasted by not using this simple technique is staggering.  (Just to make sure we all are out of the 90%, this shortcut to the Find function allows you to search locally for keywords in a text document, PDF, e-mail, or Web page.  Try it out and join the 10%.)

The second piece of the puzzle comes from reports on the findings of the Erial (Ethnographic Research in Illinois Academic Libraries) project.  The project was conducted over two years and employed both anthropologists and library staff members to examine student research habits and attitudes towards libraries and librarians.

The findings from the study will begin to be published this fall, but Steve Kolowich, writing for Inside Higher Education, notes that the researchers found that students were even worse at search than they had anticipated.  In his article, What Students Don’t Know, Kolowich discusses some of the findings, including the following;

- Only 7 out of 30 students observed at Illinois Wesleyan conducted what a librarian would consider a reasonably well-executed search.  (Presumably this encompasses such criteria as the use of appropriate search engines, Boolean logic, and authoritative sources.)

- Students mentioned Google more than twice as many times as other available databases in interviews, yet had no understanding of Google’s search logic and were not capable of formulating searches that would return good results.

- Of the students who did turn to non-Google scholarly databases for research, 50% ended up using a database that would not have been recommended by a librarian for their search needs.

- Only 3 of 30 students that were observed formulating search queries used additional keywords to narrow search queries.

- The researchers observed that students would end up with either too many or too few search results, and would often simply change their research topic to one that was easier to find information on.

It is staggeringly clear that there is a need to teach basic search techniques to students, who, without instruction, will likely go on to become part of the 90% of computer users not using simple page search.  We have created powerful search tools and technology (compared to what previous generations had available) that are capable of so much, yet we are clearly neglecting to teach proper usage and even basic understanding of search mechanics.  Even much needed advancements in search technology will not help if we are not even capable of using the tools we have now to their full potential.

Cody Burke is a senior analyst at Basex.  He can be reached at cburke@basex.com

"Are you still there? Oh well, lets wait while he dials back in...."

A few weeks ago, we ran a piece entitled Can You Hear Me Now, looking at the impact of audio problems on knowledge worker productivity.  This is Part II of that story.

Audio problems impact knowledge workers to some extent nearly every day, on nearly every phone interaction.  Typically, this results in two Information Overload-related challenges: interruptions and missed information.

In practice, interruptions in the course of a phone or conference call can take various forms, ranging from dropped calls to participants dialing in late on a conference bridge and disrupting an ongoing exchange.  Anything that causes participants to have to ask for a point to be repeated – or a technical issue to be resolved – constitutes an interruption, and each carries with it a time penalty.  This includes seemingly minor disruptions such as echo, delay, GSM static, and overtalk, because the flow of information stops when someone asks the speaker to repeat himself.  In many of these situations, callers who are experiencing problems will leave a call, switch phones, and dial in again, causing even further delays.  If the caller with the problem was the one speaking, all other callers have to wait until he or she rejoins.

Since interruptions occur numerous times each day, even when they are short (and not all are), the lost time begins to add up and becomes a significant drain on the knowledge worker’s productivity.  The impact of an interruption extends past the initial event; interviews, surveys, and first-hand observation of hundreds of knowledge workers reveal the existence of “recovery time”. Recovery time refers to the amount of time it takes a worker to get back to where he was in his work or thought process prior to an interruption.  This typically takes somewhere between 10 to 20 times the duration of the interruption itself.

On a call, the same principle holds true, except it may be amplified geometrically by the number of people impacted.  In a call, if one knowledge worker is having audio issues such as echo or delay, all participants are subjected to a time penalty.  Likely there will be a minute or two of waiting for the caller to try fix the issue, followed by some small talk, and then some brief complaining about the conferencing system.  All in all, each caller may have lost as much as five minutes, which when scaled across a conference call with many participants, can translate into significant financial losses.

A lack of clarity on a call can result in not only a time penalty as knowledge workers ask for a speaker to repeat key points, but also in missed or incorrect information.  This introduces errors into work and can lead to costly problems down the line that range from time-consuming corrections in the best case to sub-standard work product being produced in the worst case.

In a voice-only communication environment, missed or incorrect information may be due to a number of factors that range from poor quality connections that garble voices, the use of mobile phones, background noise, ambient noise picked up by a speakerphone, audio clipping due to over talking, volume inconsistency, or even simply neglecting to use the mute button when appropriate.  Some of these issues are behavioral in nature, but many are not.

The time and productivity penalty for missed information is huge.  A knowledge worker may have to go back and listen to the recording of a call (assuming it was even recorded) a second time to catch or clarify valuable information, or even engage in lengthy follow up e-mail exchanges with other participants to confirm key data.  More likely, the knowledge worker will not even be aware that information was missed, and will move forward with an action item using incomplete information.  As the project moves forward, correcting an initial error will increasingly become more costly and disruptive.

Audio quality issues can have a significant impact on knowledge worker productivity; the importance of clear communications is often overlooked, but improving audio quality will not only have a dramatic impact on productivity but may have a surprising impact on an organization’s bottom line as well.

Cody Burke is a senior analyst at Basex.  He can be reached at cburke@basex.com

Although the impact of a tool is in large part determined by the way it is used, there are certain tools that by their very design can have a negative impact on productivity.

PowerPoint, a familiar tool to all knowledge workers, has attracted a large number of detractors over the years. As the go-to business presentation tool, it is ubiquitous and almost a requirement in formal business interactions to present a slide deck with the requisite company information, colorful collection of company logos slides, series of bullet points, a few quotes, some charts, and although this thankfully seems to have fallen out of fashion, perhaps a clever animation of some sort.

Unfortunately, the metaphor of a PowerPoint presentation, that is, a linear progression of high-level talking points, is not necessarily well suited for knowledge work. Over a year ago, in April of 2010, The New York Times spoke with several military officers who blamed the tool for oversimplifying issues and fostering the illusion that people understand complex problems when they actually don’t. General James N. Mattis, U.S. Marine Corps, did not mince words when he said that “PowerPoint makes us stupid”.

The gist of the comments by military commanders in the New York Times piece was that creating the massive amount of PowerPoint slides that have become overly time-intensive and that “the program stifles discussion, critical thinking and thoughtful decision making.”

Taking the criticism of PowerPoint one step further, the Anti-PowerPoint Party (APPP) in Switzerland has recently called for a referendum on banning the use of the tool for presentations, instead advocating the use of the time-tested paper flip chart. The group claims that the use of PowerPoint costs the Swiss economy 1.2 billion Swiss Francs, or $2.5 billion. The estimated cost is based on a series of assumptions that range from the somewhat logical, that 11% of the country’s 4.1 million employees participate in PowerPoint presentations twice a week on average, and that the average number of participants is 10, to the slightly less-logical, that 85% of participants find the presentations “are killing motivation.” (To the APPP’s credit, they are very clear that they are operating on assumptions, not hard data.)

Despite the fuzzy estimation of the APPP’s economic impact, it is refreshing to see an attempt to critically examine the financial impact of a specific tool. Through extensive surveying, our research at Basex has revealed the overall cost of Information Overload to the U.S. economy in 2010 to be nearly $1 trillion. We have not looked yet at the specific cost of individual tools in that figure because we believe that the problem is a confluence of multiple factors, although isolating the cost at the tool level is an intriguing avenue of research.

While the APPP’s approach is a bit extreme, we do believe that the actions of individual knowledge workers can make a huge impact in reducing Information Overload. An outright ban on PowerPoint is draconian and an overreaction to say the least. However, if the proposed referendum spurs discussion and a reevaluation of the use of that tool, then there may be a positive impact on reducing Information Overload and increasing knowledge worker productivity. We all no doubt would be happy to sit through even one fewer PowerPoint presentation.

Cody Burke is a senior analyst at Basex.